#species_list includes Boarfish which doesn't have values, so add 'length()-1' to account for this - i.e. doesn't analyse any Boarfish data because it's nonexistent
prey_type_list <- list("benthos", "fish", "nekton", "other", "zooplankton")
for (i in 1:(length(species_list)-1)){
b=f=n=o=z=0
first_species <- renamed_df %>% filter(renamed_df$pred_species == fixed(species_list[i]))
benthos <- first_species[first_species$prey_type == fixed("benthos"),]
b <- length(benthos$haul_id)
fish <- first_species[first_species$prey_type == fixed("fish"),]
f <- length(fish$haul_id)
nekton <- first_species[first_species$prey_type == fixed("nekton"),]
n <- length(nekton$haul_id)
other <- first_species[first_species$prey_type == fixed("other"),]
o <- length(other$haul_id)
zoo <- first_species[first_species$prey_type == fixed("zooplankton"),]
z <- length(zoo$haul_id)
pie(c(b,f,n,o,z), prey_type_list, main = species_list[i])
}
These are individual pie charts showing the distribution of the type of prey each predator eats.
#Separated into individual plots for each predator species -> using facet_wrap for the variable (pred_species)
ggplot(data = renamed_df, aes(x=log(ppmr)), group=1) +
labs(title = "log(PPMR) v. biomass density of prey", x="log(PPMR)", y="Biomass density of prey") +
facet_wrap(~renamed_df$pred_species, scale="free_y") +
theme(strip.text = element_text(size = 5)) +
geom_density(aes(weight = prey_weight_g), colour="red")
#poor cod and boarfish?
for (i in 1:length(species_list)){
name <- species_list[i]
first_species <- renamed_df %>%
filter(pred_species == fixed(name))
ggplot(data = first_species, aes(x=log(ppmr)), group=1) +
labs(title = name, x="log(PPMR)", y="biomass density of prey") +
geom_density(aes(weight = prey_weight_g), colour="red") +
theme(plot.title = element_text(size=15))
av <- weighted.mean(first_species$ppmr, w = first_species$prey_weight_g, na.rm = TRUE)
stan_dev <- sd(first_species$ppmr, na.rm = TRUE)
#make standard deviation weighted by prey biomass
print(paste(name, av, stan_dev))
}
## [1] "Herring 19709.823009688 2159529.40326164"
## [1] "Sprat 195.609172891389 47666.4020722889"
## [1] "Cod 497.290047167729 5376853.35735209"
## [1] "Haddock 3022.97716139834 114469.271682299"
## [1] "Whiting 1264.39654448219 61122.0106926897"
## [1] "Blue Whiting 2010.93278075909 137888.828107828"
## [1] "Norway Pout 10973.8360655567 70337.1042810457"
## [1] "Poor Cod 517.625942013013 20871.6813906108"
## [1] "European Hake 76.7901806692788 7029.64448311904"
## [1] "Monkfish 62.9178507723842 1366.74273364163"
## [1] "Horse Mackerel 30796.6346791468 940389.877929466"
## [1] "Mackerel 205801.946353935 6441542.67335431"
## [1] "Common Dab 128.357530231156 33651.1851167703"
## [1] "Plaice 587.735382021861 250645.697372651"
## [1] "Megrim 64.4484436302096 2417.68603458976"
## [1] "Sole 274.421031981985 284930.601214936"
## [1] "Boarfish NaN NA"
Looking for the most common PPMR for each individual species.
A graph of the weighted ppmr for each species against the biomass density of the prey. Prints the mean ppmr, as weighted by prey biomass.
#Separated into individual plots for each predator species -> using facet_wrap for the variable (pred_species)
ggplot(data = renamed_df, aes(x=log(ppmr)), group=1) +
labs(title = "Scatter plot: log(PPMR) v. number density of prey", x="log(PPMR)", y="Number density of prey") +
facet_wrap(~renamed_df$pred_species, scale="free_y") +
theme(strip.text = element_text(size = 5)) +
geom_density(aes(weight = no._prey_per_stmch), colour="red")
for (i in 1:length(species_list)){
name <- species_list[i]
first_species <- renamed_df %>%
filter(pred_species == fixed(name))
ggplot(data = first_species, aes(x=log(ppmr)), group=1) +
labs(title = name, x="log(PPMR)", y="Number density of prey") +
geom_density(aes(weight = no._prey_per_stmch), colour="red") +
theme(plot.title = element_text(size=15))
av <- weighted.mean(first_species$ppmr, w = first_species$no._prey_per_stmch, na.rm = TRUE)
stan_dev <- sd(first_species$ppmr, na.rm = TRUE)
print(paste(name, av, stan_dev))
}
## [1] "Herring 4920445.2411513 2159529.40326164"
## [1] "Sprat 22863.8134490306 47666.4020722889"
## [1] "Cod 37331.0819181898 5376853.35735209"
## [1] "Haddock 143561.249495721 114469.271682299"
## [1] "Whiting 231137.0252423 61122.0106926897"
## [1] "Blue Whiting 87884.0802338307 137888.828107828"
## [1] "Norway Pout 41782.5762445494 70337.1042810457"
## [1] "Poor Cod 17279.5676618823 20871.6813906108"
## [1] "European Hake 296.484038403308 7029.64448311904"
## [1] "Monkfish 214.778052908268 1366.74273364163"
## [1] "Horse Mackerel 1656066.03448337 940389.877929466"
## [1] "Mackerel 7498547.2651935 6441542.67335431"
## [1] "Common Dab 4019.29968530871 33651.1851167703"
## [1] "Plaice 27238.3526014258 250645.697372651"
## [1] "Megrim 634.567952744918 2417.68603458976"
## [1] "Sole 32524.9032981856 284930.601214936"
## [1] "Boarfish NaN NA"
Looking for the most common PPMR for each individual species.
A graph of the weighted ppmr for each species against the number density of the prey. Prints the mean ppmr, as weighted by number of prey.
first_species <- renamed_df %>% filter(pred_species == fixed("Herring"))
#separate data set of a single species
renamed_df = renamed_df %>% mutate(lppmr = log(ppmr))
#adding a column of log(ppmr) values
herringDF <- renamed_df %>%
filter(pred_species == fixed("Herring"))
herringmean = mean(herringDF$lppmr)
herringSD = sqrt(var(herringDF$lppmr))
#these are non-weighted means
#var is variance, i.e. ave. distance from each point to the mean
#mean is the arithmetic mean of log(ppmr)
bio_herringmean = weighted.mean(herringDF$lppmr, w = herringDF$prey_weight_g, na.rm = TRUE)
bio_herringSD = sqrt(wtd.var(herringDF$lppmr, w = herringDF$prey_weight_g, na.rm = TRUE))
#mean and standard deviation, weighted by the prey biomass
ggplot(data = first_species, aes(x=log(ppmr)), group=1) +
labs(title = "Herring, weighted by prey biomass
(with a normal distribution, weighted by prey biomass)", x="log(PPMR)",y="Biomass density of observations") +
geom_density(aes(weight = prey_weight_g), colour="red") +
theme(plot.title = element_text(size=15)) +
stat_function(fun = dnorm, args= with(herringDF, c(mean = bio_herringmean, sd = bio_herringSD)))
no_herringmean = weighted.mean(herringDF$lppmr, w = herringDF$no._prey_per_stmch, na.rm = TRUE)
no_herringSD = sqrt(wtd.var(herringDF$lppmr, w = herringDF$no._prey_per_stmch, na.rm = TRUE))
#weighted standard deviation found using the weighted variance
#mean and standard deviation, weighted by the number of prey
ggplot(data = first_species, aes(x=log(ppmr)), group=1) +
labs(title = "Herring, weighted by no. of prey
(with a normal distribution, weighted by prey biomass)", x="log(PPMR)", y="No. density of observations") +
geom_density(aes(weight = no._prey_per_stmch), colour="red") +
theme(plot.title = element_text(size=15)) +
stat_function(fun = dnorm, args= with(herringDF, c(mean = no_herringmean, sd = no_herringSD)))
Plotting the distribution of Herring log(PPMR) as weighted by prey
biomass and number of prey (with weighted normal distribution curves
plotted over the top of each).
Prey biomass weighted mean: 6.629177 No. of prey weighted mean: 13.54102
The mean is shifted by 6.911843.
What does this mean?
ggplot(data = renamed_df, aes(indiv_prey_weight, no._prey_per_stmch)) +
labs(title = "prey weight v. number of prey per stomach", x="prey weight (g)", y="No. of prey per predator stomach") +
geom_point(size=0.75)
ggplot(data = renamed_df, aes(log(indiv_prey_weight), no._prey_per_stmch)) +
labs(title = "log(prey weight) v. number of prey per predator stomach", x="log(prey weight)", y="No. of prey") +
geom_point(size=0.5)
#Some interesting results --> introduce colours to show ships
renamed_df$'haul_id_short' <- gsub("\\-.*", "", renamed_df$'haul_id')
renamed_df$'haul_id_short' <- gsub("_", "", renamed_df$'haul_id_short')
#rename haul_id values -> separate by ship names (e.g. CLYDE) rather than complete id (e.g. CLYDE-1935-6)
haul_list <- unique(renamed_df$haul_id_short)
interesting_haul <- filter(renamed_df, haul_id_short=='CLYDE'|haul_id_short=='END04'|haul_id_short=='CORY08'|haul_id_short=='LUC'|haul_id_short=='HIDDINK'|haul_id_short=='EXCmacDATSTO815'|haul_id_short=="Excmacdatsto815error")
haul_list_interesting <- unique(interesting_haul$haul_id_short)
#Vertical lines: CLYDE, BLEGVARD, CIROL05, END03, LAST, LUC (*), DUBUIT, HARDY, JOHNSON
#Curves: END04
#Horizontal lines: CORY08, HIDDINK
#Identical: EXCmacDATSTO815, Excmacdatsto815error
#log(prey weight) v. number of prey per stomach - separated by ship names
for (i in 1:length(haul_list_interesting)){
haul_df <- interesting_haul %>% filter(interesting_haul$haul_id_short == fixed(haul_list_interesting[i]))
print(ggplot (data = haul_df, aes(x=log(indiv_prey_weight), y=no._prey_per_stmch)) +
labs(title = haul_list_interesting[i], x="log(prey weight)", y="No. prey per stomach") +
geom_point(size=.1, colour="red") +
theme(strip.text = element_text(size = 5)))
}
Playing around with data to see any specific correlations; what is the distribution of the weight of prey recorded.
Also, I found that the EXCmacDATSTO815 and Excmacdatsto815error gave exactly the same data (which might need to be considered in later calculations).
ggplot (data = renamed_df, aes(indiv_prey_weight, pred_weight_g)) +
geom_point(size=0.5) +
labs(title = "Predator v. prey mass plot", x="Prey mass (g)", y="Predator mass (g)")
#mass since measured in g
ggplot(data = renamed_df, aes(log(indiv_prey_weight), log(pred_weight_g))) +
geom_point(size=0.5) +
labs(title = "log(Predator mass) v. log(prey mass) plot", x="log(Prey mass)", y="log(Predator mass)") +
stat_smooth (method='lm', se=FALSE)
## `geom_smooth()` using formula 'y ~ x'
#slope <- coef(lm(log(renamed_df$pred_weight_g)~log(renamed_df$indiv_prey_weight)))
#print(paste("slope of the log(pred) v. log(prey) line of best fit:", slope))
#second part is intercept -> how to separate?
ggplot(data = renamed_df, aes(log(indiv_prey_weight), log(pred_weight_g))) +
labs(title = "log(pred. mass) v. log(prey mass) separated by predator species", x="log(prey mass)", y="log(pred. mass)") +
geom_point(size=0.2, colour="red") +
facet_wrap(~pred_species, scale="free_y") +
theme(strip.text = element_text(size = 10))
ggplot(data=renamed_df, aes(log(pred_weight_g), log(ppmr))) +
geom_point(size=0.5) +
labs(title = "log(pred mass) v. log(ppmr) plot", x="log(Pred mass)", y="log(PPMR)") +
stat_smooth (method='lm', se=FALSE)
## `geom_smooth()` using formula 'y ~ x'
slope2 <- coef(lm(log(renamed_df$ppmr)~log(renamed_df$pred_weight_g)))
print(paste("slope of the log(ppmr) v. log(pred_weight) line of best fit:", slope2))
## [1] "slope of the log(ppmr) v. log(pred_weight) line of best fit: 5.18652611475397"
## [2] "slope of the log(ppmr) v. log(pred_weight) line of best fit: 0.313443733574052"
#slope of the above plot
ggplot(data=renamed_df, aes(log(pred_weight_g), log(ppmr))) +
geom_point(size=0.5) +
labs(title = "log(pred mass) v. log(ppmr) plot", x="log(Pred mass)", y="log(PPMR)") + stat_smooth (method='lm', se=FALSE) +
facet_wrap(~pred_species, scale="free_y") +
stat_smooth(method='lm', se=FALSE)
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
species_df <- renamed_df %>% filter(pred_species == fixed("Poor Cod"))
ggplot(data=species_df, aes(log(pred_weight_g), log(ppmr))) +
geom_point(size=0.5) +
labs(title = "log(pred mass) v. log(ppmr) plot: Poor Cod", x="log(Pred mass)", y="log(PPMR)") + stat_smooth (method='lm', se=FALSE) +
facet_wrap(~pred_species, scale="free_y") +
stat_smooth(method='lm', se=FALSE)
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
species_slope <- coef(lm(log(species_df$ppmr)~log(species_df$pred_weight_g)))
print(paste("slope of the log(ppmr) v. log(pred_weight) line of best fit:", species_slope))
## [1] "slope of the log(ppmr) v. log(pred_weight) line of best fit: 6.20634582898381"
## [2] "slope of the log(ppmr) v. log(pred_weight) line of best fit: -0.0895529388001952"
#for (i in 1:length(species_list)){
# name <- species_list[i]
#first_species <- renamed_df %>% filter(pred_species == fixed(name))
#grad <- coef(lm(log(first_species$ppmr)~log(first_species$pred_weight_g)))
#print(paste(name, grad))
#}
log(pred mass) v. log(ppmr) -> is pred. mass related to ppmr?
We want them to not be proportional (i.e. slope = 0).
CHECK: IS THIS NO. OF POINTS RECORDED OR NO. POINTS*NO. PREY PER STOMACH
lmer(log(ppmr) ~ pred_species + prey_weight_g + (1|haul_id_short), data = renamed_df)
## Linear mixed model fit by REML ['lmerMod']
## Formula: log(ppmr) ~ pred_species + prey_weight_g + (1 | haul_id_short)
## Data: renamed_df
## REML criterion at convergence: 1118461
## Random effects:
## Groups Name Std.Dev.
## haul_id_short (Intercept) 1.663
## Residual 1.956
## Number of obs: 267431, groups: haul_id_short, 110
## Fixed Effects:
## (Intercept) pred_speciesCod
## 5.108989 1.413988
## pred_speciesCommon Dab pred_speciesEuropean Hake
## 0.981545 -0.577597
## pred_speciesHaddock pred_speciesHerring
## 1.916203 1.617905
## pred_speciesHorse Mackerel pred_speciesMackerel
## 1.930489 1.970072
## pred_speciesMegrim pred_speciesMonkfish
## -0.120421 -0.278221
## pred_speciesNorway Pout pred_speciesPlaice
## 2.074157 2.128633
## pred_speciesPoor Cod pred_speciesSole
## 0.648481 1.741491
## pred_speciesSprat pred_speciesWhiting
## 0.419409 0.811182
## prey_weight_g
## -0.003506
lmer(log(ppmr) ~ pred_species + prey_weight_g + (1|haul_id_short) + (1|year) + (1|no._prey_per_stmch) + (1|ices_rectangle), data = renamed_df)
## Linear mixed model fit by REML ['lmerMod']
## Formula: log(ppmr) ~ pred_species + prey_weight_g + (1 | haul_id_short) +
## (1 | year) + (1 | no._prey_per_stmch) + (1 | ices_rectangle)
## Data: renamed_df
## REML criterion at convergence: 1023959
## Random effects:
## Groups Name Std.Dev.
## no._prey_per_stmch (Intercept) 1.7994
## ices_rectangle (Intercept) 0.6634
## year (Intercept) 0.6564
## haul_id_short (Intercept) 1.6903
## Residual 1.7773
## Number of obs: 254595, groups:
## no._prey_per_stmch, 4507; ices_rectangle, 718; year, 97; haul_id_short, 97
## Fixed Effects:
## (Intercept) pred_speciesCod
## 7.354137 1.336221
## pred_speciesCommon Dab pred_speciesEuropean Hake
## 0.500668 -0.098397
## pred_speciesHaddock pred_speciesHerring
## 1.582278 1.008104
## pred_speciesHorse Mackerel pred_speciesMackerel
## 1.393272 1.341490
## pred_speciesMegrim pred_speciesMonkfish
## 0.432421 -0.132208
## pred_speciesNorway Pout pred_speciesPlaice
## 1.510116 1.853671
## pred_speciesPoor Cod pred_speciesSole
## 0.512387 1.706721
## pred_speciesSprat pred_speciesWhiting
## 0.030321 0.870806
## prey_weight_g
## -0.004578
## optimizer (nloptwrap) convergence code: 0 (OK) ; 0 optimizer warnings; 1 lme4 warnings
#lmer(fixed ~ (1|random) + linear)
Fixed effect: log(ppmr)
Random effects: haul_id_short, year
Linear fixed effects: pred_species, prey_weight_g